Class-based disentanglement methods [1]}, [2]}, [3]}, focus on producing two representations of the data \(x\) . The first contains the information relevant to the class, and the second contains the rest of the information. This second part is a representation of the data that contains all the information that is not correlated with the sensitive attribute, such as race or gender, and it is produced without the knowledge of the downstream task and labels. In our setting, we can consider the similarity of individuals only in the representation which is not correlated to the sensitive attribute.
